Spillover estimation IMC
Vito Zanotelli et al.
Aim
This script shows how to estimate spillover from single metal spots on an agarose coated slide. This short script illustrates how the helper function ‘estimate_sm_from_imc_txtfol’ can be used to estimate a spillover matrix from a folder containing all acquisitions in .txt format in one single function call.
library(CATALYST)
library(data.table)
library(ggplot2)
library(flowCore)
library(dplyr)
library(dtplyr)
library(stringr)
library(ggpmisc)
source('spillover_imc_helpers.R')setup the configuration variables
# a folder containing a complete single stain acquisition
fol_ss = '../data/Figure_S5/Spillover_Matrix_2'Example for estimation of the spillover matrix from a folder of single stains with single stains parsed from naming: xxx_x_metal_x.txt? (E.g. Dy161 1-1000_8_Dy161_8.txt)
#' estimate_sm_from_imc_txtfol
#' Estimates spillover directly from a folder containing IMC .txt files
#'
#' @param fol_ss folder containing .txt acquisitions of IMC single stains
#' @param ssmetals_from_fn logical, Are the single stains correctly named xxx_x_metal_x.txt? (E.g. Dy161 1-1000_8_Dy161_8.txt
#' @param ssmetals Vector of masses of the single stains used. Required if ssmetals_from_file_fn is False
#' @param fn2ssmetal Optional: a named vector mapping the filenames to the single stain metal used (e.g. if it cannot be parsed from the filename)
#' @param remove_incorrect_bc Remove barcodes not matching the filename single stain annotation (requires either ssmetals_from_fn=T or fn2ssmetal )
#' @param minevents Minimal number of events (after debarcoding) that need to be present in a single stain in order that a spillover estimation is performed
#' @param bin_n_pixels Optional: integer, bin n consecutive pixels. Can be used if the intensities per pixel are to low (e.g. <200 counts)
#' @param ... Optional parameters will be passed to CATALYST::computeSpillmat
res = estimate_sm_from_imc_txtfol(fol_ss, ssmetals_from_fn=T)## Debarcoding data...## o ordering## o classifying events## Normalizing...## Computing deltas...## Computing counts and yields...Plot of the spillover matrix estimated (1:10000 is just to circumvent the requirement of the plotSpillmat function to have single stain masses provided)
CATALYST::plotSpillmat(1:10000,res[['sm']])## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`Quality control: Distribution of medians per file
Note the fig.height and fig.width parameters, that can be adjusted if your own data has more facets.
plot_file_medians(res[['data']])
Additionally transforming the y axis with a squareroot transform can make the plots more readable, especially if outlier channels are acquired.
p = plot_file_medians(res[['data']])
p + scale_y_sqrt()
In some cases, e.g. when Argon was acquired, the plot above might not be informative, thus one can also calculate the medians directly and either write them to csv or use the Rstudio variable browser to inspect the results, e.g.
dat_medians = calc_file_medians(res[['data']]) %>%
dcast('metal+mass+file ~ variable', value.var='med')
dat_mediansIn case the spillover matrix should be written out
write.csv(res[['sm']],file = '../data/sm.csv')Alternatively the function can also be called with a list of single stain masses, if the metals are not contained in the filename
ssmass = c(161, 162, 163, 164, 166, 167, 168, 170, 151, 153, 155, 156, 158, 160, 165, 113, 115, 175, 142, 143, 144, 145, 146, 148, 150, 141, 147, 149, 152, 154, 159, 169, 171, 172, 173, 174, 176)
res = estimate_sm_from_imc_txtfol(fol_ss, ssmetals_from_fn=F,ssmass = ssmass, remove_incorrect_bc = F )## Debarcoding data...## o ordering## o classifying events## Normalizing...## Computing deltas...## Computing counts and yields...CATALYST::plotSpillmat(1:10000,res[['sm']])## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`sessionInfo()## R version 3.4.1 (2017-06-30)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 14.04.5 LTS
##
## Matrix products: default
## BLAS: /usr/lib/openblas-base/libblas.so.3
## LAPACK: /usr/lib/lapack/liblapack.so.3.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] stringi_1.1.7 ggpmisc_0.2.16 stringr_1.3.0
## [4] dtplyr_0.0.2 dplyr_0.7.4 flowCore_1.42.3
## [7] ggplot2_2.2.1 data.table_1.10.4-3 CATALYST_1.1.5
##
## loaded via a namespace (and not attached):
## [1] Biobase_2.36.2 httr_1.3.1 tidyr_0.8.0
## [4] viridisLite_0.3.0 jsonlite_1.5 splines_3.4.1
## [7] gtools_3.5.0 shiny_1.0.5 assertthat_0.2.0
## [10] stats4_3.4.1 yaml_2.1.18 robustbase_0.92-8
## [13] pillar_1.2.1 backports_1.1.2 lattice_0.20-35
## [16] quantreg_5.35 glue_1.2.0 digest_0.6.15
## [19] RColorBrewer_1.1-2 minqa_1.2.4 colorspace_1.3-2
## [22] sandwich_2.4-0 httpuv_1.3.6.2 htmltools_0.3.6
## [25] Matrix_1.2-13 plyr_1.8.4 pcaPP_1.9-73
## [28] pkgconfig_2.0.1 SparseM_1.77 xtable_1.8-2
## [31] purrr_0.2.4 corpcor_1.6.9 mvtnorm_1.0-7
## [34] scales_0.5.0 lme4_1.1-15 MatrixModels_0.4-1
## [37] tibble_1.4.2 mgcv_1.8-23 car_2.1-6
## [40] TH.data_1.0-8 nnet_7.3-12 BiocGenerics_0.22.1
## [43] lazyeval_0.2.1 pbkrtest_0.4-7 mime_0.5
## [46] survival_2.41-3 magrittr_1.5 evaluate_0.10.1
## [49] nlme_3.1-137 MASS_7.3-49 graph_1.54.0
## [52] tools_3.4.1 matrixStats_0.53.1 multcomp_1.4-8
## [55] plotly_4.7.1 munsell_0.4.3 cluster_2.0.7
## [58] plotrix_3.7 bindrcpp_0.2 compiler_3.4.1
## [61] rlang_0.2.0 grid_3.4.1 nloptr_1.0.4
## [64] drc_3.0-1 htmlwidgets_1.0 crosstalk_1.0.0
## [67] labeling_0.3 rmarkdown_1.9 gtable_0.2.0
## [70] codetools_0.2-15 reshape2_1.4.3 rrcov_1.4-3
## [73] R6_2.2.2 gridExtra_2.3 nnls_1.4
## [76] zoo_1.8-1 knitr_1.20 bindr_0.1.1
## [79] rprojroot_1.3-2 parallel_3.4.1 Rcpp_0.12.16
## [82] DEoptimR_1.0-8